Daily cognitive monitoring of early signs of hearing loss

ABSTRACT

In one embodiment, a computer program product includes a computer readable storage medium having program instructions embodied therewith. The embodied program instructions are executable by a processing circuit to cause the processing circuit to receive collected data from one or more data collection devices. The collected data is aggregated over a period of time lasting at least one month, and the collected data includes audio data of a user of the one or more data collection devices. The embodied program instructions also cause the processing circuit to store the audio data to a computer readable storage medium. Moreover, the embodied program instructions cause the processing circuit to analyze the audio data for indications of hearing loss in the user over the period of time.

BACKGROUND

The present invention relates to detecting early signs of hearing loss,and more specifically, to detecting early signs of hearing loss usinglong term monitoring of routine interactions and behaviors.

Hearing loss is the third most common health condition affecting olderadults, following behind hypertension and arthritis according to the NewYork Times. Hearing loss affects approximately 30-35% of adults betweenthe ages of 65 and 75 years old. Hearing loss increases as a function ofage during the aging process, and is known as presbycusis. Moreover,about 14% of adults between the ages of 45 and 64 have some amount ofhearing loss, and about eight million adults between the ages of 18 and44 have some hearing loss.

Medical guidelines indicate that adults should be screened for hearingloss every ten years through the age of 50, and at three year intervalsthereafter. However, many adults do not have regular hearing testsadministered. Some barriers to self-awareness or self-acceptance ofhearing loss include the chronic nature of the hearing loss process overmultiple years, and the hearing loss is too slow to notice. Many adultsjust assume that everyone else is mumbling. Ignorance or denial arecommon and the most important barrier to hearing aid use.

Among 2,232 retired older adults surveyed by the AmericanSpeech-Language-Hearing Association (ASHA), 76% said that their hearingwas of great importance to them; however, fewer than half of the surveyparticipants had undergone a hearing test in the past five years.Nationally, fewer than 15% of seniors aged 65 and older are believed tohave regular hearing tests.

Hearing loss also leads to poor cognitive function and aids in the onsetof dementia, according to Frank Lin, M D, et al. “Hearing Loss andIncident Dementia,” Archives of Neurology, vol. 68, no. 2 (2011). Also,people with hearing problems sometimes try to control conversations bydoing most of the talking, while others choose to withdraw fromdifficult social activities to avoid the strain and fatigue needed tohear. Hearing loss can also lead to feelings of embarrassment and shameand can affect a person's self-esteem.

Once hearing loss is found, attempts may be made to improve day-to-dayfunctioning for the person with hearing loss, but may only be attemptedonce the hearing loss is detected. Such attempts include controlling thenoise in the environment, carrying and using earplugs, keeping audioplayback device to no more than half volume (or some other safelistening level that will not damage hearing further), limiting exposuretime to loud irritating environments, etc. Moreover, attempts may bemade to learn assertive communication, e.g., allow people to get theattention of a person with hearing loss before speaking, learn how andwhen to ask to rephrase a query, etc., learn how to use visual cues,e.g., read a speaker's facial expression, body language, contextualinformation, etc., re-arranging everyday spaces, e.g., rearrangingfurniture to ensure face-to-face view is possible, changing lighting toensure good lighting on a conversation partner's face, installingcarpeting for ambient noise absorption, etc. In addition, hearingassistive technologies, hearing aids, or cochlea implants may beutilized to overcome the hearing loss.

SUMMARY

In one embodiment, a system includes a processing circuit and logicintegrated with the processor, executable by the processor, orintegrated with and executable by the processor. The logic is configuredto cause the processing circuit to receive collected data from one ormore data collection devices. The collected data is aggregated over aperiod of time lasting at least one month, and the collected dataincludes audio data of a user of the one or more data collectiondevices. The logic also causes the processing circuit to store the audiodata to a computer readable storage medium and analyze the audio datafor indications of hearing loss in the user over the period of time.

In another embodiment, a computer program product includes a computerreadable storage medium having program instructions embodied therewith.The embodied program instructions are executable by a processing circuitto cause the processing circuit to receive collected data from one ormore data collection devices. The collected data is aggregated over aperiod of time lasting at least one month, and the collected dataincludes audio data of a user of the one or more data collectiondevices. The embodied program instructions also cause the processingcircuit to store the audio data to a computer readable storage mediumand analyze the audio data for indications of hearing loss in the userover the period of time.

In yet another embodiment, a computer-implemented method includesreceiving collected data from one or more data collection devices. Thecollected data is aggregated over a period of time lasting at least onemonth, and the collected data includes audio data of a user of the oneor more data collection devices. The method also includes storing theaudio data to a computer readable storage medium and analyzing the audiodata for indications of hearing loss in the user over the period oftime.

Other aspects and embodiments of the present invention will becomeapparent from the following detailed description, which, when taken inconjunction with the drawings, illustrate by way of example theprinciples of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a network architecture, in accordance with oneembodiment.

FIG. 2 shows a representative hardware environment that may beassociated with the servers and/or clients of FIG. 1, in accordance withone embodiment.

FIG. 3 depicts a cloud computing node according to one embodiment.

FIG. 4 depicts a cloud computing environment according to oneembodiment.

FIG. 5 depicts abstraction model layers according to one embodiment.

FIG. 6 shows a system according to one embodiment.

FIG. 7 shows a flowchart of a method, according to one embodiment.

DETAILED DESCRIPTION

The following description is made for the purpose of illustrating thegeneral principles of the present invention and is not meant to limitthe inventive concepts claimed herein. Further, particular featuresdescribed herein can be used in combination with other describedfeatures in each of the various possible combinations and permutations.

Unless otherwise specifically defined herein, all terms are to be giventheir broadest possible interpretation including meanings implied fromthe specification as well as meanings understood by those skilled in theart and/or as defined in dictionaries, treatises, etc.

It must also be noted that, as used in the specification and theappended claims, the singular forms “a,” “an” and “the” include pluralreferents unless otherwise specified. It will be further understood thatthe terms “comprises” and/or “comprising,” when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof. The term“about” as used herein indicates the value preceded by the term “about,”along with any values reasonably close to the value preceded by the term“about,” as would be understood by one of skill in the art. When notindicated otherwise, the term “about” denotes the value preceded by theterm “about” ±10% of the value. For example, “about 10” indicates allvalues from and including 9.0 to 11.0.

The following description discloses several preferred embodiments ofsystems, methods, and computer program products for everyday hearingloss monitoring using data collection and analysis to discover long termtrends of hearing loss.

In one general embodiment, a system includes a processing circuit andlogic integrated with the processor, executable by the processor, orintegrated with and executable by the processor. The logic is configuredto cause the processing circuit to receive collected data from one ormore data collection devices. The collected data is aggregated over aperiod of time lasting at least one month, and the collected dataincludes audio data of a user of the one or more data collectiondevices. The logic also causes the processing circuit to store the audiodata to a computer readable storage medium and analyze the audio datafor indications of hearing loss in the user over the period of time.

In another general embodiment, a computer program product includes acomputer readable storage medium having program instructions embodiedtherewith. The embodied program instructions are executable by aprocessing circuit to cause the processing circuit to receive collecteddata from one or more data collection devices. The collected data isaggregated over a period of time lasting at least one month, and thecollected data includes audio data of a user of the one or more datacollection devices. The embodied program instructions also cause theprocessing circuit to store the audio data to a computer readablestorage medium and analyze the audio data for indications of hearingloss in the user over the period of time.

In yet another general embodiment, a computer-implemented methodincludes receiving collected data from one or more data collectiondevices. The collected data is aggregated over a period of time lastingat least one month, and the collected data includes audio data of a userof the one or more data collection devices. The method also includesstoring the audio data to a computer readable storage medium andanalyzing the audio data for indications of hearing loss in the userover the period of time.

FIG. 1 illustrates an architecture 100, in accordance with oneembodiment. As shown in FIG. 1, a plurality of remote networks 102 areprovided including a first remote network 104 and a second remotenetwork 106. A gateway 101 may be coupled between the remote networks102 and a proximate network 108. In the context of the presentarchitecture 100, the networks 104, 106 may each take any formincluding, but not limited to a LAN, a WAN such as the Internet, publicswitched telephone network (PSTN), internal telephone network, etc.

In use, the gateway 101 serves as an entrance point from the remotenetworks 102 to the proximate network 108. As such, the gateway 101 mayfunction as a router, which is capable of directing a given packet ofdata that arrives at the gateway 101, and a switch, which furnishes theactual path in and out of the gateway 101 for a given packet.

Further included is at least one data server 114 coupled to theproximate network 108, and which is accessible from the remote networks102 via the gateway 101. It should be noted that the data server(s) 114may include any type of computing device/groupware. Coupled to each dataserver 114 is a plurality of user devices 116. User devices 116 may alsobe connected directly through one of the networks 104, 106, 108. Suchuser devices 116 may include a desktop computer, lap-top computer,hand-held computer, printer or any other type of logic. It should benoted that a user device 111 may also be directly coupled to any of thenetworks, in one embodiment.

A peripheral 120 or series of peripherals 120, e.g., facsimile machines,printers, networked and/or local storage units or systems, etc., may becoupled to one or more of the networks 104, 106, 108. It should be notedthat databases and/or additional components may be utilized with, orintegrated into, any type of network element coupled to the networks104, 106, 108. In the context of the present description, a networkelement may refer to any component of a network.

According to some approaches, methods and systems described herein maybe implemented with and/or on virtual systems and/or systems whichemulate one or more other systems, such as a UNIX system which emulatesan IBM z/OS environment, a UNIX system which virtually hosts a MICROSOFTWINDOWS environment, a MICROSOFT WINDOWS system which emulates an IBMz/OS environment, etc. This virtualization and/or emulation may beenhanced through the use of VMWARE software, in some embodiments.

In more approaches, one or more networks 104, 106, 108, may represent acluster of systems commonly referred to as a “cloud.” In cloudcomputing, shared resources, such as processing power, peripherals,software, data, servers, etc., are provided to any system in the cloudin an on-demand relationship, thereby allowing access and distributionof services across many computing systems. Cloud computing typicallyinvolves an Internet connection between the systems operating in thecloud, but other techniques of connecting the systems may also be used.

FIG. 2 shows a representative hardware environment associated with auser device 116 and/or server 114 of FIG. 1, in accordance with oneembodiment. Such figure illustrates a typical hardware configuration ofa workstation having a central processing unit 210, such as amicroprocessor, and a number of other units interconnected via a systembus 212.

The workstation shown in FIG. 2 includes a Random Access Memory (RAM)214, Read Only Memory (ROM) 216, an I/O adapter 218 for connectingperipheral devices such as disk storage units 220 to the bus 212, a userinterface adapter 222 for connecting a keyboard 224, a mouse 226, aspeaker 228, a microphone 232, and/or other user interface devices suchas a touch screen and a digital camera (not shown) to the bus 212,communication adapter 234 for connecting the workstation to acommunication network 235 (e.g., a data processing network) and adisplay adapter 236 for connecting the bus 212 to a display device 238.

The workstation may have resident thereon an operating system such asthe Microsoft Windows® Operating System (OS), a MAC OS, a UNIX OS, etc.It will be appreciated that a preferred embodiment may also beimplemented on platforms and operating systems other than thosementioned. A preferred embodiment may be written using XML, C, and/orC++ language, or other programming languages, along with an objectoriented programming methodology. Object oriented programming (OOP),which has become increasingly used to develop complex applications, maybe used.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 3, a schematic of an example of a cloud computingnode is shown. Cloud computing node 310 is only one example of asuitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 310 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 310 there is a computer system/server 312, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 312 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 312 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 312 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 3, computer system/server 312 in cloud computing node310 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 312 may include, but are notlimited to, one or more processors or processing units 316, a systemmemory 328, and a bus 318 that couples various system componentsincluding system memory 328 to the one or more processors or processingunits 316.

Bus 318 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system/server 312 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 312, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 328 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 330 and/or cachememory 332. Computer system/server 312 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 334 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 318 by one or more datamedia interfaces. As will be further depicted and described below,memory 328 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 340, having a set (at least one) of program modules 342,may be stored in memory 328 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 342 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 312 may also communicate with one or moreexternal devices 314 such as a keyboard, a pointing device, a display324, etc.; one or more devices that enable a user to interact withcomputer system/server 312; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 312 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 322. Still yet, computer system/server 312can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 320. As depicted, network adapter 320communicates with the other components of computer system/server 312 viabus 318. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 312. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 4, illustrative cloud computing environment 350 isdepicted. As shown, cloud computing environment 350 includes one or morecloud computing nodes 310 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 354A, desktop computer 354B, laptop computer 354C,and/or automobile computer system 354N may communicate. Nodes 310 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 350 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 354A-Nshown in FIG. 4 are intended to be illustrative only and that computingnodes 310 and cloud computing environment 350 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 5, a set of functional abstraction layers providedby cloud computing environment 350 (FIG. 4) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 360 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 361;RISC (Reduced Instruction Set Computer) architecture based servers 362;servers 363; blade servers 364; storage devices 365; and networks andnetworking components 366. In some embodiments, software componentsinclude network application server software 367 and database software368.

Virtualization layer 370 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers371; virtual storage 372; virtual networks 373, including virtualprivate networks; virtual applications and operating systems 374; andvirtual clients 375.

In one example, management layer 380 may provide the functions describedbelow. Resource provisioning 381 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 382provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 383 provides access to the cloud computing environment forconsumers and system administrators. Service level management 384provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 385 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 390 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 391; software development and lifecycle management 392;virtual classroom education delivery 393; data analytics processing 394;transaction processing 395; and long term hearing loss monitoring andalerting 396.

Because hearing loss is such a common issue with all people as they growolder, and because it is very difficult to be self-aware of one's owngradual hearing loss, which is exacerbated by the low rate of formalhearing tests taken by seniors nationwide (less than 15%), a method fordetecting early signs of hearing loss has been designed, according toembodiments described herein, that helps users know themselves earlierwith minimal interruption in day-to-day life while still offsetting theseriously harmful effects that hearing loss makes on quality of life.

Now referring to FIG. 6, a block diagram of a system 600 is shownaccording to one embodiment. The system 600 may include one or more datacollection devices 602, each of which may have operating thereon a datacollection application 604, a normalization and labeling engine 606, acomputer readable storage medium 608, and at least one long termanalytics engine 610.

According to one embodiment, a data collection device 602 may be aneasily transportable computer hardware device that is configured tomonitor situations of a user 614, such as a conversation between theuser 614 and another person, the user 614 making or receiving atelephone call, the user 614 shopping in a supermarket, the user 614ordering food at a restaurant, playback of recorded audio contentlistened to by the user 614, or any other situation in which the hearingcapability of the user 614 may be monitored.

To this end, in various embodiments, the data collection devices 602 mayinclude a mobile telephone, a smartwatch, a tablet, a digital wearableassistant, a portable audio playback device, etc.

In one embodiment, a mobile telephone may be any smartphone or cellulartelephone known in the art that includes a microprocessor and isconfigured to execute logic, such as an APPLE iPHONE handset, a GOOGLENEXUS handset, a SAMSUNG GALAXY handset, etc.

In another embodiment, a portable audio playback device may be anyportable playback device known in the art that includes a microprocessorand is configured to execute logic to playback recorded content and/oraccessible content wirelessly, such as an APPLE iPOD, a SONY WALKMAN MP3player, a SANDISK Flash MP3 player, etc.

In accordance with another embodiment, a tablet may be any personalcomputer or computing device that is in the form of a tablet, slate, orconvertible laptop known in the art that has a flat screen andmicroprocessor and is configured to execute logic, such as an APPLEiPAD, a SAMSUNG GALAXY TAB, a MCIROSOFT SURFACE, an HP TOUCHPAD, etc.

According to another embodiment, a smartwatch may be any wrist wearablecomputing device known in the art that includes a processor therein forexecuting logic, and in some embodiments may be configured to wirelesslyconnect to a smartphone. Examples of smartwatches include an APPLEWATCH, a SAMSUNG GEAR, a SONY SMARTWATCH, an ASUS ZENWATCH, etc.

In yet another embodiment, a digital wearable assistant may be anyelectronic device that includes a hardware processor that is configuredto execute logic to collect relevant data from situations in which theuser 614 exhibits hearing capability. For example, a digital wearableassistant may include a microprocessor, a computer readable storagemedium, a microphone, and a power supply. The microprocessor may beaffixed to a button or broach and may be electrically connected to thepower supply, the computer readable storage medium, and the microphoneto enable the microprocessor to collect and store interactions made bythe user in various situations that exhibit the user's hearingcapability at any given time. Then, the microprocessor may provide thiscollected data to the data collection application 604, which may beoperating on a home computer, smartphone, or some other device regularlyvisited by the user 614. One example of a digital wearable assistant isa sociometer, as described in T. Choudhury & A. Pentland, “Sensing andmodeling human networks using the Sociometer,” ISWC 2013.

According to more embodiments, a data collection device 602 may be adevice that resides in a location normally frequented by the user 614,such as the user's home, place of business, doctor's office, vehicle,etc. In these embodiments, the data collection device 602 may be alaptop computer, a television such as a smart TV configured to collectaudio input, a surveillance system or IP camera(s), a web camera, asmart home assistant such as AMAZON ECHO, a smart home hub, an Internetof Things (IOT) processor located within a smart device, a computerrunning ALEXA software, etc.

It is preferable for the one or more data collection devices 602 to belocated and/or transportable with the user 614 to as many locations thatthe user 614 frequents as possible to provide the most data collectionopportunities available. In this way, with more and more audio inputcollected over time, smaller trends of hearing loss are able to bedetected quicker and with more accuracy, thereby allowing the hearingloss problem to be addressed sooner.

The collected data may be provided to the data collection application604 wirelessly, such as via Bluetooth, radio frequency, near fieldcommunication (NFC), wireless local area connection (WLAN), etc., or viaa hardwire connection, such as universal serial bus (USB), Ethernet, aspecialized cradle, etc.

The collected data may be sorted as audio data and metadata stored as atuple having two or more values associated together, e.g., {value1,value2}. The values may be selected to provide context to the audio dataincluded in the collected data, such as an ID of the speaker (user), aspeaking volume in which a conversation was held, another party'sspeaking volume during the conversation with the user, a listeningvolume of playback device during music listening or some other listeningactivity, a timestamp in which the data was recorded, etc.

In another embodiment, the collected data may include a tally of thenumber of times that the user 614 requested or queried for a term orphrase (e.g., spoken dialog) to be repeated, tallied over a period oftime. As the user 614 requests to repeat things more frequently, e.g.,more often per time period, it is determined that hearing loss isoccurring. This type of data may also be collected and stored to be usedto determine a user's hearing loss over a long period of time.

In one embodiment, the data collection application 604 and/or logic maystore the collected data in a local computer readable storage medium 616that is local to the one or more data collection devices 602, such asRAM, ROM, non-volatile memory (NVM) like Flash, a removable memory cardlike a SECURE DIGITAL (SD) or COMPACTFLASH, a hard disk operated by ahard disk drive (HDD), a solid state device (SSD), etc.

Moreover, in another embodiment, the data collection application 604and/or logic may store the collected data in a remote storage medium 608that is located remotely from the one or more data collection devices602, and may be accessible in some embodiments via a remote network 612,such as cloud storage, or some other remotely accessible storage mediumknown in the art.

In a further embodiment, the data collection application 604,normalization and labeling engine 606, computer readable storage medium608, and at least one long term analytics engine 610 may all be locatedremotely from the user 614 and the at least one data collection device602 and accessible via the remote network 612, such as via residing inthe cloud, utilizing distributed computing, and/or other techniquesknown in the art for removing processing and storage demands from localdevices and executing such functionality remotely.

In another embodiment, the collected data may be synchronized between alocal storage medium 616 and a remote storage medium 608, and/or theremote storage medium 608 may be updated with new data periodically fromthe local storage medium 616, thereby reducing the size of the localstorage medium 616 necessary to store the collected data, that maybecome large over extended periods of time.

The normalization and labeling engine 606 may be any computing device orlogic that is configured to take the collected data as input, analysisthe collected data to recognize different inputs (differentconversations, start and stop of hearing sessions, times when the user614 is listening to music, etc.), differentiate between types of input(e.g., audio input such as voice input from the user 614, voice inputfrom other actors, non-voice input such as music, noises, animals,etc.), determine audio data relevant to the user 614 (audio data thatprovides insight into the user's hearing ability at a particular pointin time), detect ambient noise affecting the audio data, detectbackground noise affecting the audio data, and determine metadata aboutthe user 614 of the one or more data collection devices 602, e.g., theuser's identity (name, ID number associated with the user 614, etc.),audio input timestamp, audio input duration, a transcription of theaudio data (audio data transcribed into textual data), a user's emotionduring production of the audio input, an environment of the situation inwhich the audio data was recorded, etc.

The identity of a speaker as the user 614 may be determined from theaudio input based on voice input from the user 614 that is recorded as abaseline for comparison to audio input obtained from the one or moredata collection devices 602. Moreover, in some approaches, the user 614may initiate a function or routine of the one or more data collectiondevices 602 that enables the voice input to be detected, possibly byusing a hot key press (e.g., pressing a microphone button on a keyboardof a smartphone), speaking a hot word (e.g., saying “OK Google” on adevice using the ANDROID operating system), initiating a talk-to-textfeature of the one or more data collection devices 602, etc.

The user's emotion impacts the volume level of the user's speech, andtherefore may be taken into consideration when determining whether theuser 614 is speaking with an elevated voice due to hearing loss, or dueto the emotion in the situation, e.g., the user's elevated speakingvoice may be discounted when it is determined that the user 614 isspeaking emotionally. Emotion may be inferred from a user's speech basedon recognizable patterns of elevated speech, cracking voice, emphasis onwords and parts of words, etc., that are indicators of emotion beingpresent and are not utilized during normal speaking.

Moreover, background noise and ambient noise may cause the user 614 toraise the speaking voice to be heard over the other noise, and thereforemay also be taken into consideration when determining whether the user614 is suffering from or exhibiting factors of hearing loss, e.g., theuser's elevated speaking voice may be discounted when ambient and/orbackground noise is elevated similarly.

In one embodiment, the normalization and labeling engine 606 may beconfigured to normalize all audio data with ambient noise level toprovide a consistent set of data from which hearing loss trends may bedetermined over long periods of time. For the sake of thesedescriptions, long periods of time include time frames of longer thanone month, more preferably longer than one year, and most preferablyover the course of several years or even decades. Techniques fordetecting hearing loss which are applied at singular time frames, suchas hearing tests, are useful for routine check-ups, but are not capableof providing the daily cognitive analysis which the techniques describedherein according to various embodiments enables and provides.

Moreover, in one embodiment, the normalization and labeling engine 606may be configured to label the audio data with the metadata prior tostoring the audio data to the storage medium 608. In this way, the user614 may be identified from the audio data, along with a time ofrecording for the audio data. In this way, the at least one long termanalytics engine 610 is able to analyze data from more than one userwithout the various sources of data being mixed up.

The at least one long term analytics engine 610 may include anycomputing device configured to perform analysis on the audio data storedto the storage medium 608 using any known analysis techniques available,such as regression analysis, inferential analysis, predictive analysis,casual analysis, trend estimation, mechanistic or deterministicanalysis, etc. Moreover, once analysis has been performed on the audiodata stored to the storage medium 608, the at least one long termanalytics engine 610 may be configured to alert the user 614 in responseto hearing loss being detected that exceeds a set threshold orpercentage of baseline hearing established when the monitoring isstarted.

Indications of hearing loss include, but are not limited to, elevatedspeaking or speech volume during conversations or phone calls, elevatedlistening volumes of audio playback devices, increased frequency ofqueries to repeat dialog, terms, or phrases, etc.

It is anticipated that routine hearing tests will still be undertaken bythe user 614 even when utilizing the data collection and analysistechniques described herein in various embodiments. Moreover, resultsfrom the at least one long term analytics engine 610 may be provided tothe user 614 periodically, in response to hearing loss that exceeds theset threshold or percentage, and/or on demand in response to input ofthe user 614, a request from a doctor, audiology consultant, etc.

To allow for outside intervention, the results of the at least one longterm analytics engine 610 may be provided to a doctor, audiologyconsultant, etc., in order for that medical professional to examine thehearing trends and suggest intervention and/or further examination ofthe user 614.

In one embodiment, an amount (quantitative) of hearing loss may bereported to the user 614 and/or other medical professionals, which maybe associated with a time frame of the loss, e.g., a hearing loss of 2.0decibels has been detected over the last 6 months. In a furtherembodiment, confidence scores may be calculated along with the resultsof the at least one long term analytics engine 610 in order to provide acontext for how likely it is that hearing loss has actually occurred forany given user. These confidence scores may be calculated using anyknown confidence analysis techniques, and may be presented as apercentage or some other understandable scheme, e.g., 65% likely thathearing loss of 3.5 decibels has occurred over the last 18 months. Inyet another embodiment, a message may be included in the alert topromote action on the part of the user 614, e.g., it is likely (>50%)that hearing loss has occurred—please take corrective actions to lessenadditional hearing loss.

Now referring to FIG. 7, a method 700 is shown according to oneembodiment. The method 700 may be performed in accordance with thepresent invention in any of the environments depicted in FIGS. 1-6,among others, in various embodiments. Of course, more or less operationsthan those specifically described in FIG. 7 may be included in method700, as would be understood by one of skill in the art upon reading thepresent descriptions.

Each of the steps of the method 700 may be performed by any suitablecomponent of the operating environment. For example, in variousembodiments, the method 700 may be partially or entirely performed by acloud server, a mainframe computer, a host, a processing circuit havingone or more processors therein, or some other device having one or moreprocessors therein. The processing circuit, e.g., processor(s), chip(s),and/or module(s) implemented in hardware and/or software, and preferablyhaving at least one hardware component, may be utilized in any device toperform one or more steps of the method 700. Illustrative processorsinclude, but are not limited to, a CPU, an ASIC, a FPGA, etc.,combinations thereof, or any other suitable computing device known inthe art.

As shown in FIG. 7, method 700 may start with optional operation 702,where collected data is received from one or more data collectiondevices. The collected data has been aggregated over a period of timelasting at least one month, and the collected data includes audio dataof a user of the one or more data collection devices. Moreover, in oneembodiment, a data collection application may receive the collecteddata, with the data collection application being executed locally on oneor more of the data collection devices, and/or remotely on a server,such as in a cloud-based system.

According to one embodiment, the audio data may include any of thefollowing, among other types of audio data: a conversation in which theuser participated, a telephone call received or initiated by the user,and/or playback of recorded audio content listened to by the user.

In another embodiment, the one or more data collection devices mayinclude any of the following types of devices: a mobile telephone, asmartwatch, a tablet, a digital wearable assistant, and/or a portableaudio playback device.

In operation 704, the audio data is stored to a computer readablestorage medium for later use in analysis for indications of hearingloss.

In one embodiment, the computer readable storage medium may be locatedremotely from the one or more data collection devices, such as in acloud-based system. Furthermore, the analysis may also be performedusing a processing circuit located remotely from the one or more datacollection devices.

In a further embodiment, method 700 may include determining metadataabout the audio data, and storing the metadata with the audio data as atuple having two or more values. The metadata may include anidentification of the user and a timestamp for the audio data. In afurther embodiment, the metadata may include any of the following: aspeaking volume of the user during a conversation, a second speakingvolume of another party the conversation with the user, a listeningvolume of a playback device during audio content playback, and/or atally of a number of times that the user requested for spoken dialog tobe repeated.

In operation 706, the audio data is analyzed for indications of hearingloss in the user over the period of time. In one embodiment, regressionanalysis may be performed on the audio data to determine indications ofhearing loss in the user over the period of time.

In one embodiment, method 700 may further include alerting the user ofhearing loss being detected (such as by a message on a smartphone, anemail sent to an email address of the user, an automated phone call,etc.) in response to hearing loss that exceeds a set threshold beingdetected. For example, the threshold may be 90% of a baseline hearingability measured during a routine hearing test, established with datacollected during a first period of time less than the time period overwhich the data is collected for indications of hearing loss, etc.

In one embodiment, method 700 may include identifying background noiseand/or ambient noise from the audio data. This background noise and/orambient noise may cause elevated speaking volume levels for the user incertain situations. Therefore, method 700 may also include normalizingthe audio data to account for the background noise and/or ambient noiseprior to storing the audio data.

According to another embodiment, method 700 may include determiningwhether an emotion of the user caused elevated speech volume in theaudio data. This emotion may cause elevated speaking volume levels forthe user in certain situations. For example, when a person is mad,happy, excited, etc., the person may talk louder and more forcibly in anattempt to express this emotion, get a point across in an argument,emphasize portions of dialog, etc. Therefore, method 700 may alsoinclude normalizing the audio data to account for the emotion of theuser prior to storing the audio data.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Moreover, a system according to various embodiments may include aprocessor and logic integrated with and/or executable by the processor,the logic being configured to perform one or more of the process stepsrecited herein. By integrated with, what is meant is that the processorhas logic embedded therewith as hardware logic, such as an applicationspecific integrated circuit (ASIC), a FPGA, etc. By executable by theprocessor, what is meant is that the logic is hardware logic; softwarelogic such as firmware, part of an operating system, part of anapplication program; etc., or some combination of hardware and softwarelogic that is accessible by the processor and configured to cause theprocessor to perform some functionality upon execution by the processor.Software logic may be stored on local and/or remote memory of any memorytype, as known in the art. Any processor known in the art may be used,such as a software processor module and/or a hardware processor such asan ASIC, a FPGA, a central processing unit (CPU), an integrated circuit(IC), a graphics processing unit (GPU), etc.

It will be clear that the various features of the foregoing systemsand/or methodologies may be combined in any way, creating a plurality ofcombinations from the descriptions presented above.

It will be further appreciated that embodiments of the present inventionmay be provided in the form of a service deployed on behalf of acustomer to offer service on demand.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

What is claimed is:
 1. A system, comprising: a processing circuit andlogic integrated with the processing circuit, executable by theprocessing circuit, or integrated with and executable by the processingcircuit, the logic being configured to cause the processing circuit to:obtain baseline hearing ability for a user; receive collected data fromone or more data collection devices after obtaining the baselinehearing, the collected data being aggregated over a period of timelasting at least one month, wherein the collected data comprises audiodata of the user of the one or more data collection devices; analyze theaudio data for indications of hearing loss in the user over the periodof time, wherein the logic configured to cause the processing circuit toanalyze the audio data comprises logic configured to cause theprocessing circuit to: determine whether an emotion of the user and/orat least one noise caused an elevated speech volume, wherein the emotionis determined based on a trait not utilized during normal speaking, andwherein the trait is selected from the group consisting of: a crackingvoice of the user, an emphasis on words that indicate the emotion, anemphasis on words that indicate the emotion, an emphasis on parts ofwords that indicate the emotion, and combinations thereof; identify theat least one noise from the audio data, the at least one noise beingselected from the group consisting of: background noise, ambient noise,and combinations thereof; and normalize the audio data to account forthe emotion of the user and the identified at least one noise, anddiscount the elevated speech volume accordingly, prior to storing theaudio data to a computer readable storage medium; and analyze the storedaudio data for one or more indicia of hearing loss; and alert the userof the one or more indicia of hearing loss being detected in response todetermining hearing loss that exceeds a predetermined thresholdpercentage of the baseline hearing.
 2. The system as recited in claim 1,wherein the logic is further configured to cause the processing circuitto: determine metadata about the audio data, the metadata comprising atleast an identification of the user and a timestamp for the audio data;and store the metadata with the audio data as a tuple having two or morevalues.
 3. The system as recited in claim 2, wherein the metadatafurther comprises a tally of a number of times that the user requestedfor spoken dialog to be repeated over a plurality of periods of time,and wherein an increasing tally of the number of times that the userrequested for spoken dialog to be repeated over several time periods isindicative of hearing loss occurring; and wherein the logic is furtherconfigured to cause the processing circuit to quantify an amount of thehearing loss experienced by the user over a predetermined amount oftime.
 4. The system as recited in claim 1, wherein the audio data isselected from the group consisting of: a conversation in which the userparticipated, a telephone call received or initiated by the user,playback of recorded audio content listened to by the user, andcombinations thereof.
 5. The system as recited in claim 1, wherein theone or more data collection devices are selected from the groupconsisting of: a mobile telephone, a smartwatch, a tablet, a digitalwearable assistant, a portable audio playback device, and combinationsthereof.
 6. The system as recited in claim 1, wherein the thresholdpercentage is about 90% of the baseline hearing ability of the user. 7.The system as recited in claim 1, wherein the logic is furtherconfigured to cause the processing circuit to: determine metadata aboutthe audio data, the metadata comprising at least an identification ofthe user and a timestamp for the audio data; store the metadata with theaudio data as a tuple having two or more values, wherein the metadatafurther comprises a tally of a number of times that the user requestedfor spoken dialog to be repeated over a plurality of periods of time,and wherein an increasing tally of the number of times that the userrequested for spoken dialog to be repeated over several time periods isindicative of hearing loss occurring; perform regression analysis on theaudio data to determine indications of hearing loss in the user over theperiod of time; wherein the audio data is selected from the groupconsisting of: a conversation in which the user participated, atelephone call received or initiated by the user, playback of recordedaudio content listened to by the user, and combinations thereof; whereinthe one or more data collection devices are selected from the groupconsisting of: a mobile telephone, a smartwatch, a tablet, a digitalwearable assistant, a portable audio playback device, and combinationsthereof; and wherein the computer readable storage medium is locatedremotely from the one or more data collection devices.
 8. A computerprogram product, the computer program product comprising a computerreadable storage medium having program instructions embodied therewith,the embodied program instructions being executable by a processingcircuit to cause the processing circuit to: obtain, by the processingcircuit, baseline hearing for a user; receive, by the processingcircuit, collected data from one or more data collection devices afterobtaining the baseline hearing, the collected data being aggregated overa period of time lasting at least one month, wherein the collected datacomprises audio data of the user of the one or more data collectiondevices; analyze, by the processing circuit, the audio data forindications of hearing loss in the user over the period of time, whereinthe program instructions configured to cause the processing circuit toanalyze the audio data comprises program instructions configured tocause the processing circuit to: determine, using the processingcircuit, whether an emotion of the user and/or at least one noise causedan elevated speech volume, wherein the emotion is determined based on atrait not utilized during normal speaking, and wherein the trait isselected from the group consisting of: a cracking voice of the user, anemphasis on words that indicate the emotion, an emphasis on words thatindicate the emotion, an emphasis on parts of words that indicate theemotion, and combinations thereof; identify, using the processingcircuit, the at least one noise from the audio data, the at least onenoise being selected from the group consisting of: background noise,ambient noise, and combinations thereof; and normalize, using theprocessing circuit, the audio data to account for the emotion of theuser and the identified at least one noise, and discount the elevatedspeech volume accordingly, prior to storing the audio data to thecomputer readable storage medium; and analyze, using the processingcircuit, the stored audio data for one or more indicia of hearing loss;and alert, by the processing circuit, the user of the one or moreindicia of hearing loss being detected in response to determininghearing loss that exceeds a predetermined threshold percentage of thebaseline hearing being detected as a result of analyzing the audio data.9. The computer program product as recited in claim 8, wherein theembodied program instructions are further executable by the processingcircuit to cause the processing circuit to: determine, by the processingcircuit, metadata about the audio data; and store, by the processingcircuit, the metadata with the audio data as a tuple having two or morevalues, wherein the metadata comprises an identification of the user, atimestamp for the audio data, a speaking volume of the user during aconversation, and a tally of a number of times that the user requestedfor spoken dialog to be repeated over a plurality of periods of time,and wherein an increasing tally of the number of times that the userrequested for spoken dialog to be repeated over several time periods isindicative of hearing loss occurring.
 10. The computer program productas recited in claim 8, wherein the audio data is selected from the groupconsisting of: a conversation in which the user participated, atelephone call received or initiated by the user, playback of recordedaudio content listened to by the user, and combinations thereof; whereinthe computer readable storage medium is located remotely from the one ormore data collection devices; and wherein the one or more datacollection devices are selected from the group consisting of: a mobiletelephone, a smartwatch, a tablet, a digital wearable assistant, aportable audio playback device, and combinations thereof.
 11. Thecomputer program product as recited in claim 8, wherein the embodiedprogram instructions executable to cause the processing circuit to:analyze, by the processing circuit, the audio data for indications ofhearing loss in the user over the period of time further causes theprocessing circuit to perform regression analysis on the audio data todetermine indications of hearing loss in the user over the period oftime; and quantify, by the processing circuit, an amount of the hearingloss experienced by the user over a predetermined amount of time.
 12. Acomputer-implemented method, the method comprising: obtaining baselinehearing ability for a user; receiving collected data from one or moredata collection devices after obtaining the baseline hearing, thecollected data being aggregated over a period of time lasting at leastone month, wherein the collected data comprises audio data of the userof the one or more data collection devices; analyzing the audio data forindications of hearing loss in the user over the period of time, whereinanalyzing the audio data comprises: determining whether an emotion ofthe user and/or at least one noise caused an elevated speech volume,wherein the emotion is determined based on a trait not utilized duringnormal speaking, and wherein the trait is selected from the groupconsisting of: a cracking voice of the user, an emphasis on words thatindicate the emotion, an emphasis on words that indicate the emotion, anemphasis on parts of words that indicate the emotion, and combinationsthereof; identifying the at least one noise from the audio data, the atleast one noise being selected from the group consisting of: backgroundnoise, ambient noise, and combinations thereof; and normalizing theaudio data to account for the emotion of the user and the identified atleast one noise, and discount the elevated speech volume accordingly,prior to storing the audio data to a computer readable storage medium;and analyzing the stored audio data for one or more indicia of hearingloss; and alerting the user of hearing loss being detected in responseto hearing loss that exceeds a predetermined threshold percentage of thebaseline hearing ability being detected as a result of analyzing theaudio data.
 13. The method as recited in claim 12, further comprising:determining metadata about the audio data; and storing the metadata withthe audio data as a tuple having two or more values, wherein themetadata comprises information selected from the group consisting of: anidentification of the user, a timestamp for the audio data, a speakingvolume of the user during a conversation, and a tally of a number oftimes that the user requested for spoken dialog to be repeated over aplurality of periods of time, and wherein an increasing tally of thenumber of times that the user requested for spoken dialog to be repeatedover several time periods is indicative of hearing loss occurring. 14.The method as recited in claim 12, wherein the audio data is selectedfrom the group consisting of: a conversation in which the userparticipated, a telephone call received or initiated by the user,playback of recorded audio content listened to by the user, andcombinations thereof; wherein the computer readable storage medium islocated remotely from the one or more data collection devices; andwherein the one or more data collection devices are selected from thegroup consisting of: a mobile telephone, a smartwatch, a tablet, adigital wearable assistant, a portable audio playback device.